Thank You for your explanation.
For example:
First I’ve run standard OPF on case13 but without any branch constraints (
Rate A in branch data is 9999 ) and generator cost data looks as shown below:
gencost = [
2 0.00 0.00 3 0.000000 90.000000 0.000000;
2 0.00 0.00 3 0.000000 70.000000 0.000000;
2 0.00 0.00 3 0.000000 60.000000 0.000000;
2 0.00 0.00 3 0.000000 80.000000 0.000000;
2 0.00 0.00 3 0.000000 50.000000 0.000000;
];
As a result P1 = 30MW ; P2 = 60MW ; P3 = 60MW ; P6 = 45.74MW and P7 = 60MW
Next I’ve set constraints on branches and created new gencost matrix using
piecewise linear cost.
Because all generators are limited Pmin = 30MW ; Pmax = 60MW I haven’t changed
cost for P1 (becuse P1 = Pmin ) but for generators P2, P3, P7 I’ve changed cost
to 0 (because P2 = P3 = P7 = Pmax). For P6 from 0 – 45.74MW cost is 0 and from
45.74 – 60MW cost is 80.
With this settings I’ve run OPF.
As a result P1 = 30MW ; P2 = 57.25MW ; P3 = 60MW ; P6 = 52.69MW and P7 = 56.71MW
And these values are minimum control change, am I correct ?
Beat Regaeds
Przemek Lipiecki
Dnia 5 lipca 2011 19:28 Ray Zimmerman <[email protected]> napisał(a):
> MATPOWER only solves OPF problems with continuous variables. I believe that
> minimum control changes is implicitly a discrete variable problem. A possibly
> related continuous variable problem would be to minimize the change in
> dispatch from a given dispatch point. This can be accomplished by shifting
> the portion of the each cost curve to the right of the dispatch point up by
> some amount (an incremental cost) and the portion to the left down by some
> amount (a decremental cost).
>
>
